jigsaw_ai_safety_keras/Evaluating_Toxicity_Datasets_Large_Language_Models.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e4ylwxOxOs3J"
      },
      "source": [
        "# Evaluating Toxicity in Datasets used by Large Language Models"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KkwgnQEMO8yk"
      },
      "source": [
        "Today we see Large Language Models (LLMs) revolutionizing the Deep Learning landscape. LLMs are knowledge interpolation machines that can further be finetuned for specialised tasks in various industries.\n",
        "\n",
        "\n",
        "However, LLMs learn to generate the types of knowledge present in their training data. Data determines the quality/characteristics of LLM responses. LLM outputs are negatively affected by the data containing unsafe examples.\n",
        "\n",
        "A toxic conversation (i.e., a data point) annotated as safe for LLM training, will have collateral impact on all LLM responses and further fine-tunes of that model.\n",
        "\n",
        "Aligning datasets to ensure safer LLM responses is the need of the hour. This process typically depends on teams of human data annotators that review vast amounts of data to flag unsafe examples, inevitably making many mistakes in these annotations.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eHic3k_cbfKD"
      },
      "source": [
        "![toxic_convo.png](data:image/png;base64,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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "This notebook showcases how Cleanlab can help you find and fix such mistakes in such a dataset. We use the [Civil Comments dataset](https://www.tensorflow.org/datasets/catalog/civil_comments), originated from the comments section on independent news sites.\n",
        "\n",
        "The Civil Comments dataset is a diverse collection of user comments, covering many topics and discussions and reflecting the varied nature of online discourse.\n",
        "\n",
        "[Jigsaw](https://jigsaw.google.com/), a unit within Google that explores threats to open societies, extended this dataset by have a team of data annotators label the examples in this dataset for: toxicity, identity mentions, as well as covert offensiveness.\n",
        "The annotations in this dataset provide valuable insights into the prevalence and nature of harmful behavior online.\n",
        "\n",
        "Here we specifically focus on the annotations of toxicity levels in user comments, but the same ideas demonstrated in this notebook can be applied to other AI safety annotations for any text dataset. Each comment is labeled with a toxicity score indicating the degree of toxicity or offensiveness exhibited in the comment. This annotation enables researchers to analyze patterns of toxic behavior and develop models for detecting and mitigating toxicity in online conversations, or can be used to filter the unsafe content from this dataset prior to model training."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LA8XlbwEyClX"
      },
      "source": [
        "## Installing and import dependencies"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LNktNppgLAW7",
        "outputId": "83878bfd-6e41-4c07-e0c3-9f043858ee84"
      },
      "outputs": [],
      "source": [
        "!pip install -q --upgrade keras-nlp\n",
        "!pip install -q --upgrade keras\n",
        "!pip install -q cleanlab-studio"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YiaeBmPLLYgj",
        "outputId": "787ca02f-f1e3-4746-bd29-7e21a52e30a6"
      },
      "outputs": [],
      "source": [
        "!pip install -q datasets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "i-_YsBQwLe_e"
      },
      "outputs": [],
      "source": [
        "import keras_nlp\n",
        "import keras\n",
        "import tensorflow as tf\n",
        "import os\n",
        "import pandas as pd\n",
        "\n",
        "keras.utils.set_random_seed(42)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FNJHjPNFRT93"
      },
      "source": [
        "## Load Dataset\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xy8eNedGHiCj"
      },
      "source": [
        "We obtain the Jigsaw dataset from [TensorFlow Datasets](https://www.tensorflow.org/datasets/catalog/civil_comments), which provides preprocessed and ready-to-use datasets for machine learning tasks."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 169,
          "referenced_widgets": [
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        },
        "id": "Kr_ZJt25LT24",
        "outputId": "f43fc45e-04a7-4c4e-9b2d-14666249c321"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Downloading and preparing dataset 427.41 MiB (download: 427.41 MiB, generated: 1.54 GiB, total: 1.95 GiB) to /root/tensorflow_datasets/civil_comments/CivilComments/1.2.4...\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d8d3597074bd4d2ea41381b01193bb67",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Dl Completed...: 0 url [00:00, ? url/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "37bb3eba82734ad2a2abb929091edfc6",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Dl Size...: 0 MiB [00:00, ? MiB/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "9c7044a70c3e4347900f0c889ecc8e75",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Extraction completed...: 0 file [00:00, ? file/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e69ec63453634f3f9f9254e8d1b80c35",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Generating splits...:   0%|          | 0/3 [00:00<?, ? splits/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "acdcc6f170b84dcb9ab41b4d3a467750",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Generating train examples...:   0%|          | 0/1804874 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "384b0fbe0afc4c8fbe75cc015064e0c4",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Shuffling /root/tensorflow_datasets/civil_comments/CivilComments/1.2.4.incompleteG8F1CB/civil_comments-train.t…"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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              "model_id": "e65351196bc645efbcc8a435adda8007",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Generating validation examples...:   0%|          | 0/97320 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "06ab873468084ea69054eaa9073e1530",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Shuffling /root/tensorflow_datasets/civil_comments/CivilComments/1.2.4.incompleteG8F1CB/civil_comments-validat…"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "cff390ba64e04f7e8fb007b0e72437c4",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Generating test examples...:   0%|          | 0/97320 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d6a152f58e63499684e039fe42e44041",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Shuffling /root/tensorflow_datasets/civil_comments/CivilComments/1.2.4.incompleteG8F1CB/civil_comments-test.tf…"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Dataset civil_comments downloaded and prepared to /root/tensorflow_datasets/civil_comments/CivilComments/1.2.4. Subsequent calls will reuse this data.\n"
          ]
        }
      ],
      "source": [
        "import tensorflow_datasets as tfds\n",
        "\n",
        "# Construct a tf.data.Dataset\n",
        "ds = tfds.load('civil_comments', split='train')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LVjbX0vLy05H"
      },
      "source": [
        "## Dataset Structure"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RWAp89jSTINi"
      },
      "source": [
        "A sample of Jigsaw Civil Comments Dataset structure with columns would look like below.\n",
        "\n",
        "The `text` column likely contains the actual text of the comment, while the other columns provide information about the degree of `toxicity`, `severe toxicity`, and `obscenity` present in the comment.\n",
        "\n",
        "\n",
        "You can explore more about the dataset from the Dataframe below.\n",
        "\n",
        "| text                                                                                                               | toxicity | severe_toxicity | obscene  | threat | insult  | identity_attack | sexual_explicit |\n",
        "|--------------------------------------------------------------------------------------------------------------------|----------|-----------------|----------|--------|---------|-----------------|-----------------|\n",
        "| Thank you!! This would make my life a lot less anxiety-inducing. Keep it up, and don't let anyone get in your way! |        0 |               0 |        0 |      0 |       0 |               0 |               0 |\n",
        "| haha you guys are a bunch of losers.                                                                               | 0.893617 |        0.021277 |        0 |      0 | 0.87234 |        0.021277 |               0 |\n",
        "| This is such an urgent design problem; kudos to you for taking it on. Very impressive!                             |        0 |               0 |        0 |      0 |       0 |               0 |               0 |\n",
        "| Is this something I'll be able to install on my site? When will you be releasing it?                               |        0 |               0 |        0 |      0 |       0 |               0 |               0 |\n",
        "| hahahahahahahahhha suck it.                                                                                        | 0.457627 |        0.050847 | 0.305085 |        |         |                 |                 |"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qFzCRnQB5ppO"
      },
      "outputs": [],
      "source": [
        "df = pd.DataFrame(ds.take(1000))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "-h_rRdoK8tRR",
        "outputId": "fe3a91fc-81f9-4397-9cd4-91214828bfa9"
      },
      "outputs": [
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If the court can prove them she should not be allowed to plea down for the convenience of the court. Anything they cannot prove they should drop.'\",\n          \"b'\\\"I\\\\'m sure you know that my post was serious; perhaps labeling it as unserious is your way of not dealing with it. My first point simply goes to the hypocrisy of persons who, on grounds of \\\\'religious liberty\\\\', seek to restrict the liberty of other persons.\\\"\\\\n\\\\nYOU\\\\'RE not dealing with it.  You assume there is a true liberty for two gays to marry.  There isn\\\\'t --- short of perverting the Constitution beyond recognition, which the Supreme Court did.  Reasonable people can, and do, dispute the \\\"liberty\\\" of gay marriage.  It does not seriously meet their argument to say, \\\"Then don\\\\'t marry someone of the same sex.\\\"  That doesn\\\\'t meet their reasonable objection, which is that marriage has always meant the union of people of the opposite sex.  If your post were serious, you would not have proposed as a solution something that doesn\\\\'t resolve the objections one iota.  And if you think your first \\\"solution\\\" was \\\"serious,\\\" you don\\\\'t understand the objection.\\\\n\\\\n\\\"No-one\\\" is sloppy and non-standard.'\",\n          \"b'Maybe you should put a call into your former teammate, Sakic, then Peter. I see Duchene handling this situation as professionally as he possibly can while Sakic fiddly farts around seeking way too much in return for a trade. Sakic has missed his window and dented the value of a productive member of the team with his continuing indecisiveness.'\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"publication_id\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 15,\n        \"samples\": [\n          \"b'66'\",\n          \"b'100'\",\n          \"b'54'\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"severe_toxicity\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"num_unique_values\": 24,\n        \"samples\": [\n          0.012500000186264515,\n          0.014084506779909134,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sexual_explicit\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"num_unique_values\": 22,\n        \"samples\": [\n          0.0,\n          0.5,\n          0.01666666753590107\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1000,\n        \"samples\": [\n          \"b'Surprise, surprise! It did not take a study to prove the diversity in the RC church. All you have to do is look around!'\",\n          \"b\\\"Geez, HPD should also tell these Pro and non-Pro beggars off the medians who are trying to collect money. I'm kona complain the next coming Elections of all the sign wavers as they are VERY distracting! Count on it. Finally I see light of HPD doing something right for the vehicle drivers on the roadways who have enough to worry about besides distracting Political people waving their signs and watnots\\\"\",\n          \"b'I am one of those recipients.\\\\n\\\\nAt age 72, you get to get some of the money back you paid in to support those who cam before you.'\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"threat\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"num_unique_values\": 23,\n        \"samples\": [\n          0.014285714365541935,\n          0.04285714402794838,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"toxicity\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"num_unique_values\": 46,\n        \"samples\": [\n          0.41333332657814026,\n          0.08695652335882187,\n          0.3636363744735718\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
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              "      <td>5388737</td>\n",
              "      <td>b\"Here's a vote for Comey to be prosecuting fo...</td>\n",
              "      <td>b'55'</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>b\"You don't understand what leaking is.  By th...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>163084</td>\n",
              "      <td>b'2017-01-20 23:46:35.985660+00'</td>\n",
              "      <td>b'871483'</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>870487</td>\n",
              "      <td>b\"Excuse me, but what religious freedom are yo...</td>\n",
              "      <td>b'53'</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>b'duties come before rights.\\nthey have a duty...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>161284</td>\n",
              "      <td>b'2017-01-11 19:39:41.734415+00'</td>\n",
              "      <td>b'825427'</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>825241</td>\n",
              "      <td>b'Who thought it would be a good idea to put R...</td>\n",
              "      <td>b'22'</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>b'Based upon the activities of the OHA I have ...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>995</th>\n",
              "      <td>351199</td>\n",
              "      <td>b'2017-07-03 20:38:01.662587+00'</td>\n",
              "      <td>b'5522278'</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>b''</td>\n",
              "      <td>b'102'</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>b'Emperor Nero reincarnate.'</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>996</th>\n",
              "      <td>154860</td>\n",
              "      <td>b'2016-12-06 16:17:41.283990+00'</td>\n",
              "      <td>b'669270'</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>b''</td>\n",
              "      <td>b'54'</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>b'No question about it. It should be referred ...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>997</th>\n",
              "      <td>160869</td>\n",
              "      <td>b'2017-01-12 01:54:34.698581+00'</td>\n",
              "      <td>b'827390'</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>b''</td>\n",
              "      <td>b'21'</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>b\"Official global population estimate for pola...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>998</th>\n",
              "      <td>145440</td>\n",
              "      <td>b'2016-09-07 18:17:30.262194+00'</td>\n",
              "      <td>b'464265'</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>464081</td>\n",
              "      <td>b'My thoughts are these people that profited d...</td>\n",
              "      <td>b'13'</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>b'Finally:\\n\\n The names would be easy to find...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>999</th>\n",
              "      <td>366358</td>\n",
              "      <td>b'2017-08-15 04:44:17.847326+00'</td>\n",
              "      <td>b'5770257'</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5766496</td>\n",
              "      <td>b\"You may be right on this one, Michael, but o...</td>\n",
              "      <td>b'53'</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>b\"But it's a start....\"</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1000 rows × 14 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-47d2fa76-e309-4f4b-baba-a33ab04fff2b')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-47d2fa76-e309-4f4b-baba-a33ab04fff2b button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-47d2fa76-e309-4f4b-baba-a33ab04fff2b');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-08c7905c-60ec-4fc7-bc19-1cdeafaf43e9\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-08c7905c-60ec-4fc7-bc19-1cdeafaf43e9')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-08c7905c-60ec-4fc7-bc19-1cdeafaf43e9 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "     article_id                      created_date          id  \\\n",
              "0        153145  b'2016-11-29 17:23:57.762283+00'   b'634903'   \n",
              "1        379147  b'2017-09-19 03:02:05.207449+00'  b'5977874'   \n",
              "2        342612  b'2017-06-10 06:32:21.121964+00'  b'5390534'   \n",
              "3        163084  b'2017-01-20 23:46:35.985660+00'   b'871483'   \n",
              "4        161284  b'2017-01-11 19:39:41.734415+00'   b'825427'   \n",
              "..          ...                               ...         ...   \n",
              "995      351199  b'2017-07-03 20:38:01.662587+00'  b'5522278'   \n",
              "996      154860  b'2016-12-06 16:17:41.283990+00'   b'669270'   \n",
              "997      160869  b'2017-01-12 01:54:34.698581+00'   b'827390'   \n",
              "998      145440  b'2016-09-07 18:17:30.262194+00'   b'464265'   \n",
              "999      366358  b'2017-08-15 04:44:17.847326+00'  b'5770257'   \n",
              "\n",
              "     identity_attack  insult  obscene  parent_id  \\\n",
              "0                0.0     0.0      0.0          0   \n",
              "1                0.0     0.0      0.0    5977787   \n",
              "2                0.0     0.7      0.0    5388737   \n",
              "3                0.0     0.0      0.0     870487   \n",
              "4                0.0     0.0      0.0     825241   \n",
              "..               ...     ...      ...        ...   \n",
              "995              0.0     0.0      0.0          0   \n",
              "996              0.0     0.0      0.0          0   \n",
              "997              0.0     0.0      0.0          0   \n",
              "998              0.0     0.0      0.0     464081   \n",
              "999              0.0     0.0      0.0    5766496   \n",
              "\n",
              "                                           parent_text publication_id  \\\n",
              "0                                                  b''          b'54'   \n",
              "1    b\"I get the impression that Boeing  is very af...          b'54'   \n",
              "2    b\"Here's a vote for Comey to be prosecuting fo...          b'55'   \n",
              "3    b\"Excuse me, but what religious freedom are yo...          b'53'   \n",
              "4    b'Who thought it would be a good idea to put R...          b'22'   \n",
              "..                                                 ...            ...   \n",
              "995                                                b''         b'102'   \n",
              "996                                                b''          b'54'   \n",
              "997                                                b''          b'21'   \n",
              "998  b'My thoughts are these people that profited d...          b'13'   \n",
              "999  b\"You may be right on this one, Michael, but o...          b'53'   \n",
              "\n",
              "     severe_toxicity  sexual_explicit  \\\n",
              "0                0.0              0.0   \n",
              "1                0.0              0.0   \n",
              "2                0.0              0.0   \n",
              "3                0.0              0.0   \n",
              "4                0.0              0.0   \n",
              "..               ...              ...   \n",
              "995              0.0              0.0   \n",
              "996              0.0              0.0   \n",
              "997              0.0              0.0   \n",
              "998              0.0              0.0   \n",
              "999              0.0              0.0   \n",
              "\n",
              "                                                  text  threat  toxicity  \n",
              "0    b\"btw, Globe, your new comment section is lame...     0.0       0.2  \n",
              "1    b\"If at first you don't succeed...try again: h...     0.0       0.0  \n",
              "2    b\"You don't understand what leaking is.  By th...     0.0       0.7  \n",
              "3    b'duties come before rights.\\nthey have a duty...     0.0       0.0  \n",
              "4    b'Based upon the activities of the OHA I have ...     0.0       0.0  \n",
              "..                                                 ...     ...       ...  \n",
              "995                       b'Emperor Nero reincarnate.'     0.0       0.0  \n",
              "996  b'No question about it. It should be referred ...     0.0       0.0  \n",
              "997  b\"Official global population estimate for pola...     0.0       0.0  \n",
              "998  b'Finally:\\n\\n The names would be easy to find...     0.0       0.0  \n",
              "999                            b\"But it's a start....\"     0.0       0.0  \n",
              "\n",
              "[1000 rows x 14 columns]"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "def mp(x):\n",
        "    return x.numpy()\n",
        "df = df.map(mp)\n",
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 521
        },
        "id": "XXGJ7kFd-UmS",
        "outputId": "3f2b852d-55ec-43d9-d4b9-b9c21d8fdcfa"
      },
      "outputs": [
        {
          "data": {
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If the court can prove them she should not be allowed to plea down for the convenience of the court. Anything they cannot prove they should drop.\",\n          \"\\\"I'm sure you know that my post was serious; perhaps labeling it as unserious is your way of not dealing with it. My first point simply goes to the hypocrisy of persons who, on grounds of 'religious liberty', seek to restrict the liberty of other persons.\\\"\\n\\nYOU'RE not dealing with it.  You assume there is a true liberty for two gays to marry.  There isn't --- short of perverting the Constitution beyond recognition, which the Supreme Court did.  Reasonable people can, and do, dispute the \\\"liberty\\\" of gay marriage.  It does not seriously meet their argument to say, \\\"Then don't marry someone of the same sex.\\\"  That doesn't meet their reasonable objection, which is that marriage has always meant the union of people of the opposite sex.  If your post were serious, you would not have proposed as a solution something that doesn't resolve the objections one iota.  And if you think your first \\\"solution\\\" was \\\"serious,\\\" you don't understand the objection.\\n\\n\\\"No-one\\\" is sloppy and non-standard.\",\n          \"Maybe you should put a call into your former teammate, Sakic, then Peter. I see Duchene handling this situation as professionally as he possibly can while Sakic fiddly farts around seeking way too much in return for a trade. Sakic has missed his window and dented the value of a productive member of the team with his continuing indecisiveness.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"publication_id\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 15,\n        \"samples\": [\n          \"66\",\n          \"100\",\n          \"54\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"severe_toxicity\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"num_unique_values\": 24,\n        \"samples\": [\n          0.012500000186264515,\n          0.014084506779909134,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sexual_explicit\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"num_unique_values\": 22,\n        \"samples\": [\n          0.0,\n          0.5,\n          0.01666666753590107\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1000,\n        \"samples\": [\n          \"Surprise, surprise! It did not take a study to prove the diversity in the RC church. All you have to do is look around!\",\n          \"Geez, HPD should also tell these Pro and non-Pro beggars off the medians who are trying to collect money. I'm kona complain the next coming Elections of all the sign wavers as they are VERY distracting! Count on it. Finally I see light of HPD doing something right for the vehicle drivers on the roadways who have enough to worry about besides distracting Political people waving their signs and watnots\",\n          \"I am one of those recipients.\\n\\nAt age 72, you get to get some of the money back you paid in to support those who cam before you.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"threat\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"num_unique_values\": 23,\n        \"samples\": [\n          0.014285714365541935,\n          0.04285714402794838,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"toxicity\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"num_unique_values\": 46,\n        \"samples\": [\n          0.41333332657814026,\n          0.08695652335882187,\n          0.3636363744735718\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
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            "text/plain": [
              "   article_id                   created_date       id  identity_attack  \\\n",
              "0      153145  2016-11-29 17:23:57.762283+00   634903              0.0   \n",
              "1      379147  2017-09-19 03:02:05.207449+00  5977874              0.0   \n",
              "2      342612  2017-06-10 06:32:21.121964+00  5390534              0.0   \n",
              "3      163084  2017-01-20 23:46:35.985660+00   871483              0.0   \n",
              "4      161284  2017-01-11 19:39:41.734415+00   825427              0.0   \n",
              "\n",
              "   insult  obscene  parent_id  \\\n",
              "0     0.0      0.0          0   \n",
              "1     0.0      0.0    5977787   \n",
              "2     0.7      0.0    5388737   \n",
              "3     0.0      0.0     870487   \n",
              "4     0.0      0.0     825241   \n",
              "\n",
              "                                         parent_text publication_id  \\\n",
              "0                                                                54   \n",
              "1  I get the impression that Boeing  is very afra...             54   \n",
              "2  Here's a vote for Comey to be prosecuting for ...             55   \n",
              "3  Excuse me, but what religious freedom are you ...             53   \n",
              "4  Who thought it would be a good idea to put Row...             22   \n",
              "\n",
              "   severe_toxicity  sexual_explicit  \\\n",
              "0              0.0              0.0   \n",
              "1              0.0              0.0   \n",
              "2              0.0              0.0   \n",
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              "4              0.0              0.0   \n",
              "\n",
              "                                                text  threat  toxicity  \n",
              "0  btw, Globe, your new comment section is lame. ...     0.0       0.2  \n",
              "1  If at first you don't succeed...try again: htt...     0.0       0.0  \n",
              "2  You don't understand what leaking is.  By the ...     0.0       0.7  \n",
              "3  duties come before rights.\\nthey have a duty t...     0.0       0.0  \n",
              "4  Based upon the activities of the OHA I have al...     0.0       0.0  "
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import numpy as np\n",
        "for col, dtype in df.dtypes.items():\n",
        "    if dtype == object:  # Only process byte object columns.\n",
        "        df[col] = df[col].apply(lambda x: x.decode(\"utf-8\"))\n",
        "\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 521
        },
        "id": "E3s6V64BCLU5",
        "outputId": "e1aa07bf-58d0-4ae9-9ec9-3fbc07b98436"
      },
      "outputs": [
        {
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If the court can prove them she should not be allowed to plea down for the convenience of the court. Anything they cannot prove they should drop.\",\n          \"\\\"I'm sure you know that my post was serious; perhaps labeling it as unserious is your way of not dealing with it. My first point simply goes to the hypocrisy of persons who, on grounds of 'religious liberty', seek to restrict the liberty of other persons.\\\"\\n\\nYOU'RE not dealing with it.  You assume there is a true liberty for two gays to marry.  There isn't --- short of perverting the Constitution beyond recognition, which the Supreme Court did.  Reasonable people can, and do, dispute the \\\"liberty\\\" of gay marriage.  It does not seriously meet their argument to say, \\\"Then don't marry someone of the same sex.\\\"  That doesn't meet their reasonable objection, which is that marriage has always meant the union of people of the opposite sex.  If your post were serious, you would not have proposed as a solution something that doesn't resolve the objections one iota.  And if you think your first \\\"solution\\\" was \\\"serious,\\\" you don't understand the objection.\\n\\n\\\"No-one\\\" is sloppy and non-standard.\",\n          \"Maybe you should put a call into your former teammate, Sakic, then Peter. I see Duchene handling this situation as professionally as he possibly can while Sakic fiddly farts around seeking way too much in return for a trade. Sakic has missed his window and dented the value of a productive member of the team with his continuing indecisiveness.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"publication_id\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 15,\n        \"samples\": [\n          \"66\",\n          \"100\",\n          \"54\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"severe_toxicity\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"num_unique_values\": 24,\n        \"samples\": [\n          0.012500000186264515,\n          0.014084506779909134,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sexual_explicit\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"num_unique_values\": 22,\n        \"samples\": [\n          0.0,\n          0.5,\n          0.01666666753590107\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1000,\n        \"samples\": [\n          \"Surprise, surprise! It did not take a study to prove the diversity in the RC church. All you have to do is look around!\",\n          \"Geez, HPD should also tell these Pro and non-Pro beggars off the medians who are trying to collect money. I'm kona complain the next coming Elections of all the sign wavers as they are VERY distracting! Count on it. Finally I see light of HPD doing something right for the vehicle drivers on the roadways who have enough to worry about besides distracting Political people waving their signs and watnots\",\n          \"I am one of those recipients.\\n\\nAt age 72, you get to get some of the money back you paid in to support those who cam before you.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"threat\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"num_unique_values\": 23,\n        \"samples\": [\n          0.014285714365541935,\n          0.04285714402794838,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"toxicity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
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              "    fill: var(--disabled-fill-color);\n",
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              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
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              "</div>\n",
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            ],
            "text/plain": [
              "   article_id                   created_date       id  identity_attack  \\\n",
              "0      153145  2016-11-29 17:23:57.762283+00   634903              0.0   \n",
              "1      379147  2017-09-19 03:02:05.207449+00  5977874              0.0   \n",
              "2      342612  2017-06-10 06:32:21.121964+00  5390534              0.0   \n",
              "3      163084  2017-01-20 23:46:35.985660+00   871483              0.0   \n",
              "4      161284  2017-01-11 19:39:41.734415+00   825427              0.0   \n",
              "\n",
              "   insult  obscene  parent_id  \\\n",
              "0     0.0      0.0          0   \n",
              "1     0.0      0.0    5977787   \n",
              "2     0.7      0.0    5388737   \n",
              "3     0.0      0.0     870487   \n",
              "4     0.0      0.0     825241   \n",
              "\n",
              "                                         parent_text publication_id  \\\n",
              "0                                                                54   \n",
              "1  I get the impression that Boeing  is very afra...             54   \n",
              "2  Here's a vote for Comey to be prosecuting for ...             55   \n",
              "3  Excuse me, but what religious freedom are you ...             53   \n",
              "4  Who thought it would be a good idea to put Row...             22   \n",
              "\n",
              "   severe_toxicity  sexual_explicit  \\\n",
              "0              0.0              0.0   \n",
              "1              0.0              0.0   \n",
              "2              0.0              0.0   \n",
              "3              0.0              0.0   \n",
              "4              0.0              0.0   \n",
              "\n",
              "                                                text  threat  toxicity  \n",
              "0  btw, Globe, your new comment section is lame. ...     0.0         0  \n",
              "1  If at first you don't succeed...try again: htt...     0.0         0  \n",
              "2  You don't understand what leaking is.  By the ...     0.0         1  \n",
              "3  duties come before rights.\\nthey have a duty t...     0.0         0  \n",
              "4  Based upon the activities of the OHA I have al...     0.0         0  "
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "def fn(x):\n",
        "  if x>0.3: return 1\n",
        "  return 0\n",
        "df['toxicity'] = df['toxicity'].apply(fn)\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "73ZQPW-n17g_"
      },
      "outputs": [],
      "source": [
        "df.to_csv('jigsaw.csv')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XuCic0pWV31M"
      },
      "source": [
        "## Data Curation using Cleanlab Studio\n",
        "\n",
        "Cleanlab Studio is a powerful tool designed to help you improve the quality of your data for machine learning projects. It tackles the challenge of messy real-world data, which often contains errors, inconsistencies, and biases that can lead to poor model performance.\n",
        "Cleanlab Studio contains a novel data-centric AI system that can automatically detect problems in datasets and suggest remedies."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bfheFOhUR351"
      },
      "source": [
        "Now that we have our dataset, let’s load it into Cleanlab Studio and conduct our analysis.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FQyeeY07OSbR"
      },
      "outputs": [],
      "source": [
        "from cleanlab_studio import Studio\n",
        "\n",
        "# you can find your Cleanlab Studio API key by going to studio.cleanlab.ai/upload,\n",
        "# clicking \"Upload via Python API\", and copying the API key there\n",
        "\n",
        "API_KEY = \"YOUR API KEY\"\n",
        "\n",
        "studio = Studio(API_KEY)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "laHrQ_ImUBdz"
      },
      "source": [
        "Let's load the Jigsaw dataset into Cleanlab Studio and create a Project which will automatically detect issues with this dataset."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9wvXELR4SKau"
      },
      "source": [
        "### Load data into Cleanlab Studio"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ts6kOI5uP_Ud",
        "outputId": "0da024ff-1487-404a-c4f5-9dc381a4ec65"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Uploading dataset...: 100%|██████████|\n",
            "Ingesting Dataset...: 100%|██████████|\n"
          ]
        }
      ],
      "source": [
        "dataset_id = studio.upload_dataset('jigsaw.csv', dataset_name=\"jigsaw\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2DKcB8gMSPpS"
      },
      "source": [
        "### Launch a Project"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jxibbjHDQbL0",
        "outputId": "bdb0333f-a3c1-40be-9131-d90f18457698"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Text column not supplied. Using best guess text\n",
            "Project successfully created and training has begun! project_id: 42c1eb38f4e247778c026fc9022ae4d7\n"
          ]
        }
      ],
      "source": [
        "project_id = studio.create_project(\n",
        "    dataset_id=dataset_id,\n",
        "    project_name=\"jigsaw\",\n",
        "    modality=\"text\",\n",
        "    task_type=\"multi-class\",\n",
        "    model_type=\"fast\",\n",
        "    label_column='toxicity'\n",
        ")\n",
        "print(f\"Project successfully created and training has begun! project_id: {project_id}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NRJCB4UtSYlq"
      },
      "source": [
        "> Note: For big datasets, this next cell may take a long time to execute while Cleanlab’s AI model is training. If your Jupyter notebook has timed out during this process, you can resume work by re-running the below cell (which should return instantly if the project has completed training). You don't create a new project."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K4chiih_Q-U-",
        "outputId": "01c3b626-0ba9-4f8d-fdaa-82fe47e9391d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "cleanset_id: 5b10408b233d4ce6b001af5232a37383\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Cleanset Progress: \\ Step 53/53, Ready for review!\n"
          ]
        }
      ],
      "source": [
        "cleanset_id = studio.get_latest_cleanset_id(project_id)\n",
        "print(f\"cleanset_id: {cleanset_id}\")\n",
        "project_status = studio.wait_until_cleanset_ready(cleanset_id)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UP1a1pr6UWkl"
      },
      "source": [
        "After fetching the cleaned dataset (called a *Cleanset* for short), let's merge it with our original dataset.\n",
        "\n",
        "Cleanlab Studio helps resolve multiple data issues like label issues, outliers and ambiguous data. It uses ensemble of models like Gradient Boosted Trees, Neural Networks, Foundation Models, etc. combined with novel data quality algorithms to identify which common problems are present in this dataset. The platform also present AI suggested fixes for these data problems like which data points to remove from the dataset or which labels to correct.\n",
        "\n",
        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HR9Trzk0BuTLAIWUJhuP+qfYJF9sI133wXfHU785Lnqfnky/+Wvs/MHr4oH98vUANLjdYmw69n+XprPiA/mj1qU02Thy4tP5EgCrlCYbjxjUYayQJhtPfkdTnHPuD2O9dLqrKd61R3nS8bHYpWVd6/RcPJWsmfd/z8VppTsBQENqvDEcH3+gdHXjiO1j66wAACiSs864LpYf9U8RbyzKS9r6zof+KTZp+lX80815wc3nxPVNfWP/D43OCwCgXqQnG34sRk6MGLZzXgRAp77wmaNi2RGDkljhzbyk3F4xbGDEU3/4ZXwtL4mpv4zb50bsvM1eecHKvnP24Nj5jaa47Nt/yksAoDE1SMLx3bH1iOTmuk/H4E99Lys55NARMXvCrnFMduXjgTVxViq0aro3zj3oYzG8ZSrvGilbNy7uXtFjallZ6erGK56KmDbxsx12ndpRl6pZl6utr9f2udN15/7s3piYry+NgwpQq0bHyTtEPHT7cTFgxt/zsnKHxfb9IuYt+Gu+XHLaX5LlfpvHWfkyANSFx++IK5LvvjvuHx+j3puXAdCJveLk90T88q4fxqZ/WJyXlStdvbjLj/PFrvjEv8bJA9+MXz5QlqQEoNXuW23VoxO1pUESjpvFkV+5KA7Jl2LERXHJ0ZvF1u8Zns0/eu5u+QqoAY9PjuEnTI5tx/80pt6fTpfEsXdd0MXxWAbF4dd8K764c8Swsd/qQteppTOix8Ql+Wsl0/jBccUJbZOO0yY+HNveVFo/dve8EKAmTY5/Ov3UOPCX+eJK3h3v2jDib6/eky+X2XCT2CmfBYC6sPtowyUAdFmaULw9DpyaL3ZJ6arHea8tyJfL7RQP/FO/iLlzuvmcAI1h9LBh8a2jP96jU/oa1I7G6VJ1i6NiUss4G9cdFZsnRZsfPbF1HmrFk7++N+KIS8oSe0Nj7PhDI+6aFk/mJVXT9LuY8tShMemMoXlBYvfRMemIp+OKH5UlOHfeN/5lUD4PAAAAUHg7xQMXr6K71OHvin/e8M345aOP5QUA0NgabwxHqGlN8fyfI4ZtMzBfzu0+LI6N5+L58m5Uq2HO7JgW98aY1u5US9OYu/L1Ld67lbOmAQAAgAaRJhvfH/ttuCC+e2HH3aV+4X3vjE3e+Hv8t6sbATr0zWnPxkk/frRHp/Q1qB0FTjjOiIk77hqDuzRdFb/LHwU1qWlOzMpnq6l59nPJ/4fGpJbuVMun8qseAQrjr/G3NyLetelh+XKZN+ZFB+ctA9DoXro9xnQYR6aTWBKgeMqSjef+d5yWl7a1U/zrlm+PeS/+zdiNAKvw29nNPTpRW1zhCDVlUGz33ohpL8zNl3PZlYiDY7vOujXN1nffgK0HJ//3wJWTADXrnvjLgohN+r07Xy75zvbJ8oKX46p8GQBKXok7v3xR/CJfAqDo8mRjNMU5nSYbU/1ii3Rs+L87ZRGgU2/ltz1pbbwGXVbghONuMbZlzMbVTmfFB/JHwbq2y4fS8RoviImP5wUxMyaOS8d1HBa7pIuDtopto3yMxaa4+3vJ+ladJC07svvI+OLOyXNddm+sOB8keb2DPhbn/kwWEiim0379h5g36MPxh+PzguOvjFMGLYqHfj05LwCAFn+N2VOSmxEXxaNiSYDC+87ZebKxk25UWw3fKN4Vb8ZLf8uXAQBXOELN2X10TL1pdMwa1zKm4gXxwyMuKevidGiMTdYPu+uCfP3XI05MlvO1qZak5fCDJseTeVnHBsXh13wrvhiTY2Q+fmP6erPGfismfLSzyykB6twvL4sBv/lr7PzBW2L5tcn0wXfHU785NQ78Zb4eAFq9O7YeEXHIoSNi87wEgIIavl8cNTC53XBQXDnh2FhWPl28X3yhdK+Sd20Ym8Ti+IvxGwGgVa+3Evl8t6QPS6fly5dH84JFMaD/xvkaoNEsXboszrn1ppj4m/vzEgAAKjX7y9+IQZttki/1vOb5C2NAv77Ru3fv6NWrVzb1pLqLJR+/KgZ/KuLWAlzNmB63Pz+nKY667eaYPW9eXlq5u04+NfbZfvvYccI9Mevvr+elAADUsvtP2y9GDO750+m++T/Pxjen/Tlf6hmfG/be+Ny/7JAvsTZ1FEe6whEAAAA6NCMmfup7ye334pgdd43BK01Xxe9KdwQAAGhoEo4AAAAAAACwls1f8FpcdNlVMWv2nLykfkk4AgAAQId2i7HPPBHPdTrVfzerAAAA1SDhmHj5ttt1gwMAAAAAAFBQP7//V3HcSWOz6TOnf7HNVYVPPPlUh+vSKxAnfP2b8YOf/Cxbl87f/fMHsuWz//PSrCx9bGpVz5/Op2Ut69P7ps996fir45k/PxfnXXB56/PUq8ZJOL50e4zpcMyNXWPv8/6S3wkAAADaefyq1vhxzG2vxMu3jY3Bp94eL+erAQCA2pYm8x745dSY9P+Nj1tumBhjP/Nvce2kG7KkX7pu4rf/X1x+yXmt6/5rwrWtCcO5L78abyxanK079/Ofiz7r9YlfTZkWp485KSvbdZedswTiE0/+Mb77ra+t9PzplM6nZem69D2k72XWC3Pi/HFnxo7vHZy9dvo89axBEo6vxJ1fvih+kS+1d9YPdIMDAADAyrLk4qe+ly+VmXJR7D1hRr4AAADUqsVLlsTP7/9lHLjf8Ojf7x1ZWZrc++p/nR/rr//2bN3Hjjw0tt16q9Z1e+/xz/HgQ49ky6k9dt8tnyt5z+BtY9DAzbP5NKH4m8cej08ec2RssP76WVn6HO8bskNMe/TxbBq4+aax4w6Ds3Xpe0hfu94TjO01SMLxrzF7SnJz6vfjuWe+H2elRdn8E3HrqRFXTXJmKgAAAO3NiB+fNzVixEXxaB4/pjY/emJp/roHDM8BAAA1bsmSN7OrFLd816C8ZIXO1m255Rb53OrNmz8//vbS3Kxb1JYuU9PpgV8lsUTixRdfis0GDGhNRhZVQ43heMh73p38v1sMSwPDZ/4vSzJu/Z7hEVP+ErPTOwAAAMBqvRJznslnAQCAhrfxxhu1dslaPn3koA/n9yi+Bkk4lpKMv7h3SpZk/MABJ5a6v8nGb0zPVt0+ti7dEQAAAHK7xScuT09SLcWPx1yXxJXn7R+Dd9w/zpwSccjlJxqeAwAAalzabWrapemLf2vKS1bobF16VWJXbdK/f/Tq1Svmz38tL2krvVrylebmrGvXImuYKxw/cO5DcXVcFBfc9krE7ifG1SPyFTE8rv7KUVHqaRcAAABWSLtPfe4HJ+ZLKxxy+UMx6ejN8iUAAKBWpV2ZfuSg/eKBX07NxltMzZo9J84675Jomvtytu6nd96blaWeePKpeHT6/8YB+++TLa9OOiZjOj7kjbfc2vr86e3Z/3lp9lzD9t4967b1mWefy9aliccJX/9m/Pz+X2XLRdFAXapuFkde90QeEJbm0zEcn3tmYhzZ9a54AQAAaDS7n5XHjysmyUYAAKgfu+6yc5YUHPMf47LxFdPxFk88/uOx7dZbZevGfubfWsdgnPjt/xf/ee7p2bquSrtOfd+QHVqfP70dddwx2XOnCcnTx5yUPW+67uTPfiEb0zF9TLpum63enb12vScge72VyOe7JX1YOi1fvjyaFyyKAf03ztfUohkxccdPx1Wnfj+eO3e3vCz1Stx56v5xZlwUj17nKkeo1NKly+KcW2+Kib+5Py8BAKBSs7/8jRi02Sb5Us9rnr8wBvTrG7179866AUqnnlRfsWSxpMftz89piqNuuzlmz5uXl1burpNPjX223z52nHBPzPr763kpAAC17P7T9osRg3s+G/LN/3k2vjntz/lSz/jcsPfG5/5lh3yJtamjOLKBrnDsyGax1Y7JzZS/xOxSAQAAAOTSk1d3jcHpNGFGXgYAAEB7xU44Pn5VKTBMr25Ml6/7dL68YkoH/QcAAIBVao0nx8adL+VlAAAAZIqdcNz9xLh6RD6/CodcfmJ8IJ8HAACAkt1ibMu4jT84MS+bGmeOyE9iPfX2eDkvBQAAaGQF71J1szjyujQ4/H6clS6mYziWDfLfMhnsHwAAgFXa/azWGPLRy4eXygzPAQAAkGmQMRzzs1LP3S1fbuvl226P3+XzAAAAsJLWITt2jb3Pm1oqO/VAveUAAAAkGiThmHjp9hiTB4ftp73P+0t+JwAAAGgxIya2xI6f+l5eNjyunpL3mNPJSa0AAACNpkESjq/EnV++KH6RL7V31g/OclYqAAAAnTrk8ofyLlUnxpFb5IUAAABker2VyOe7JX1YOi1fvjyaFyyKAf03ztfUovSs1E/HVekYjudG2fxu8bsJu8Yxz1wUj153VGye3xvonqVLl8U5t94UE39zf14CAEClZn/5GzFos03ypZ7XPH9hDOjXN3r37h29evXKpp5UX7FksaTH7c/PaYqjbrs5Zs+bl5dW7q6TT419tt8+dpxwT8z6++t5KQAAtez+0/aLEYN7Phvy2OzmfK5n7bX1gHyOtamjOLJxulRNHPKedyf/7xbDTk1unvm/eDm52fo9ww30DwAAAAAAUCVpInBtTNSOBkk4lpKMv7h3SpZk/MABJ0ZMuSj2zsZvnBoxYvvYunRHAAAAGlrLuI1Xxe/Kx3DscErvAwAAQMNc4fiBcx+Kq+OiuOC2VyJ2PzGuHpGvSAf8/4ruVAEAAAAAAKASDTKGI9CTjOEIAFA9xnCkpxjDEQCAtTWGI8XW8GM4AgAAAAAAANXVGAnHx6/qYKyNXWPi4/l6AAAAaKM0fuOYdFiOeCXuPLVdTDlhRuluAAAAFD3hmAeFn/pevtzWVZ9K1p16e7ycLwMAAEC8dHuM2fHTcVUy+4t7p3QcM143Oe58KZ8HAABocIVOOP5uwv5x5pR07sS49Zkn4rk200Nx9Yhk1ZSL4oLsjFUAAABIYskbL4pfZHNJLHndUbFihJtSbHnrqen81DjzRlc5AgAApAqccHwl5jyT3qYB4Vnxgays3GZx5HXfj7OSuU7PWAUAAKDBzIhp16W3w+Pq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      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "W194vf2LRYs9",
        "outputId": "0d21c875-26ea-47ab-9ab3-1a645782dc34"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
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\"column\": \"near_duplicate_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.1884606364102908,\n        \"min\": 0.0,\n        \"max\": 0.6913305202000001,\n        \"num_unique_values\": 945,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"near_duplicate_cluster_id\",\n      \"properties\": {\n        \"dtype\": \"Int64\",\n        \"num_unique_values\": 0,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"is_outlier\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"outlier_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.04827540124635525,\n        \"min\": 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              "  <thead>\n",
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              "      <th>cleanlab_row_ID</th>\n",
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              "      <th>is_initially_unlabeled</th>\n",
              "      <th>has_rare_class</th>\n",
              "      <th>is_empty_text</th>\n",
              "      <th>text_num_characters</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>&lt;NA&gt;</td>\n",
              "      <td>False</td>\n",
              "      <td>0.151326</td>\n",
              "      <td>&lt;NA&gt;</td>\n",
              "      <td>0.848674</td>\n",
              "      <td>False</td>\n",
              "      <td>0.998227</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>0.503121</td>\n",
              "      <td>&lt;NA&gt;</td>\n",
              "      <td>False</td>\n",
              "      <td>0.010710</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>216</td>\n",
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              "      <td>False</td>\n",
              "      <td>0.125007</td>\n",
              "      <td>&lt;NA&gt;</td>\n",
              "      <td>0.874993</td>\n",
              "      <td>False</td>\n",
              "      <td>0.995130</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>0.226294</td>\n",
              "      <td>&lt;NA&gt;</td>\n",
              "      <td>False</td>\n",
              "      <td>0.027774</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>171</td>\n",
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              "      <td>0.422958</td>\n",
              "      <td>&lt;NA&gt;</td>\n",
              "      <td>0.577042</td>\n",
              "      <td>False</td>\n",
              "      <td>0.829061</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>0.533229</td>\n",
              "      <td>&lt;NA&gt;</td>\n",
              "      <td>False</td>\n",
              "      <td>0.009060</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>353</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
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              "      <td>&lt;NA&gt;</td>\n",
              "      <td>False</td>\n",
              "      <td>0.011410</td>\n",
              "      <td>&lt;NA&gt;</td>\n",
              "      <td>0.988590</td>\n",
              "      <td>False</td>\n",
              "      <td>0.963953</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>0.223576</td>\n",
              "      <td>&lt;NA&gt;</td>\n",
              "      <td>False</td>\n",
              "      <td>0.021266</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>72</td>\n",
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              "      <td>&lt;NA&gt;</td>\n",
              "      <td>False</td>\n",
              "      <td>0.057661</td>\n",
              "      <td>&lt;NA&gt;</td>\n",
              "      <td>0.942339</td>\n",
              "      <td>False</td>\n",
              "      <td>0.980173</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>0.357548</td>\n",
              "      <td>&lt;NA&gt;</td>\n",
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              "      <td>0.014321</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
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            ],
            "text/plain": [
              "   cleanlab_row_ID corrected_label  is_label_issue  label_issue_score  \\\n",
              "0                1            <NA>           False           0.151326   \n",
              "1                2            <NA>           False           0.125007   \n",
              "2                3            <NA>           False           0.422958   \n",
              "3                4            <NA>           False           0.011410   \n",
              "4                5            <NA>           False           0.057661   \n",
              "\n",
              "  suggested_label  suggested_label_confidence_score  is_ambiguous  \\\n",
              "0            <NA>                          0.848674         False   \n",
              "1            <NA>                          0.874993         False   \n",
              "2            <NA>                          0.577042         False   \n",
              "3            <NA>                          0.988590         False   \n",
              "4            <NA>                          0.942339         False   \n",
              "\n",
              "   ambiguous_score  is_well_labeled  is_near_duplicate  near_duplicate_score  \\\n",
              "0         0.998227            False              False              0.503121   \n",
              "1         0.995130            False              False              0.226294   \n",
              "2         0.829061            False              False              0.533229   \n",
              "3         0.963953            False              False              0.223576   \n",
              "4         0.980173            False              False              0.357548   \n",
              "\n",
              "   near_duplicate_cluster_id  is_outlier  outlier_score  \\\n",
              "0                       <NA>       False       0.010710   \n",
              "1                       <NA>       False       0.027774   \n",
              "2                       <NA>       False       0.009060   \n",
              "3                       <NA>       False       0.021266   \n",
              "4                       <NA>       False       0.014321   \n",
              "\n",
              "   is_initially_unlabeled  has_rare_class  is_empty_text  text_num_characters  \n",
              "0                   False           False          False                  216  \n",
              "1                   False           False          False                  171  \n",
              "2                   False           False          False                  353  \n",
              "3                   False           False          False                   72  \n",
              "4                   False           False          False                  173  "
            ]
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "cleanlab_columns_df = studio.download_cleanlab_columns(cleanset_id)\n",
        "cleanlab_columns_df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mHb2aLRXRftM"
      },
      "outputs": [],
      "source": [
        "df = pd.read_csv('jigsaw.csv')\n",
        "combined_dataset_df = df.merge(cleanlab_columns_df, left_index=True, right_on=\"cleanlab_row_ID\")\n",
        "combined_dataset_df.rename(columns={\"toxicity\": \"given_label\"}, inplace=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G3DuhwyByRF1"
      },
      "source": [
        "By using Cleanlab studio, we can identify the given label correctness (toxicity) in our dataset along with the other recommended corrections by Cleanlab Studio like Near duplicates in dataset etc."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 660
        },
        "id": "VxCHU-LUyFzZ",
        "outputId": "b6c224f0-44f1-4a26-a8c0-dfd914b57fd3"
      },
      "outputs": [
        {
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>article_id</th>\n",
              "      <th>created_date</th>\n",
              "      <th>id</th>\n",
              "      <th>identity_attack</th>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Advanced dementia and the possibility of starv...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.987626</td>\n",
              "      <td>True</td>\n",
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              "      <td>0.0</td>\n",
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              "     Unnamed: 0  article_id                   created_date       id  \\\n",
              "241         242      319096  2017-03-15 06:44:47.834860+00  5006354   \n",
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              "237         238      156365  2016-12-15 14:13:25.958009+00   713170   \n",
              "\n",
              "     identity_attack  insult  obscene  parent_id  \\\n",
              "241              0.0     0.0      0.0          0   \n",
              "583              0.0     0.0      0.0          0   \n",
              "774              0.0     0.0      0.0    5649291   \n",
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              "\n",
              "                                           parent_text  publication_id  \\\n",
              "241                                                NaN              54   \n",
              "583                                                NaN              54   \n",
              "774  I see the TSX just dropped over 50 points  so ...              54   \n",
              "24   A nice picture of Tulsi and her mentor Jane Fo...              55   \n",
              "237  Let's turn all of our citizens into drug addic...              54   \n",
              "\n",
              "     severe_toxicity  sexual_explicit  \\\n",
              "241              0.0              0.0   \n",
              "583              0.0              0.0   \n",
              "774              0.0              0.0   \n",
              "24               0.0              0.0   \n",
              "237              0.0              0.0   \n",
              "\n",
              "                                                  text  threat  \\\n",
              "241  My parents were the most incredibly resilient ...     0.0   \n",
              "583  Advanced dementia and the possibility of starv...     0.0   \n",
              "774  CR: \"I see the TSX just dropped over 50 points...     0.0   \n",
              "24   Vietnam was an extremely costly war, one that ...     0.0   \n",
              "237  \"Let's turn all of our citizens into drug addi...     0.0   \n",
              "\n",
              "     label_issue_score  is_label_issue  given_label suggested_label  \n",
              "241           0.995842            True            0               0  \n",
              "583           0.987626            True            0               0  \n",
              "774           0.985476            True            0               0  \n",
              "24            0.984683            True            0               0  \n",
              "237           0.983719            True            0               0  "
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "DATASET_COLUMNS = df.columns.drop(\"toxicity\").tolist()\n",
        "samples_ranked_by_label_issue_score = combined_dataset_df.sort_values(\"label_issue_score\", ascending=False)\n",
        "\n",
        "columns_to_display = DATASET_COLUMNS + [\"label_issue_score\", \"is_label_issue\", \"given_label\", \"suggested_label\"]\n",
        "display(samples_ranked_by_label_issue_score.head(5)[columns_to_display])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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        },
        "id": "wNg2-fNHyGp4",
        "outputId": "c02359d6-4b37-4939-e658-2bd401b1420b"
      },
      "outputs": [
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "\n",
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              "\n",
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              "      --bg-color: #E8F0FE;\n",
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              "\n",
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              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    animation:\n",
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              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
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              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "    (() => {\n",
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              "</div>\n",
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            ],
            "text/plain": [
              "     Unnamed: 0  article_id                   created_date       id  \\\n",
              "963         964      372276  2017-08-31 09:16:25.405738+00  5872468   \n",
              "31           32      347554  2017-06-23 16:41:08.020298+00  5465676   \n",
              "418         419      374368  2017-09-06 21:25:37.565928+00  5910128   \n",
              "163         164      320117  2017-03-17 16:52:37.363282+00  5018236   \n",
              "77           78      329017  2017-04-25 12:07:36.115950+00  5176008   \n",
              "\n",
              "     identity_attack  insult  obscene  parent_id  \\\n",
              "963              0.0     0.0      0.0    5869781   \n",
              "31               0.0     0.0      0.0    5462097   \n",
              "418              0.0     0.0      0.0    5904446   \n",
              "163              0.0     0.0      0.0    5018166   \n",
              "77               0.0     0.0      0.0    5175049   \n",
              "\n",
              "                                           parent_text  publication_id  \\\n",
              "963  CO₂'s radiative effect is only band saturated ...             102   \n",
              "31   ms.amylou since you seem to be an expert on ed...              21   \n",
              "418  Trevorlittle is Trevor Siemian when he plays c...             102   \n",
              "163  Read through the article in the link that was ...              54   \n",
              "77   I disagree, they labeled  an undefined term in...              54   \n",
              "\n",
              "     severe_toxicity  sexual_explicit  \\\n",
              "963              0.0              0.0   \n",
              "31               0.0              0.0   \n",
              "418              0.0              0.0   \n",
              "163              0.0              0.0   \n",
              "77               0.0              0.0   \n",
              "\n",
              "                                                  text  threat  \\\n",
              "963  Dilution: only 1/2500 of the atmosphere is com...     0.0   \n",
              "31   It would be helpful if we tried to see both si...     0.0   \n",
              "418  Man, you're just a overflowing jug of optimism...     0.0   \n",
              "163                                   FACT CHECK FALSE     0.0   \n",
              "77                    Oh the horror. Oh the hyperbole.     0.0   \n",
              "\n",
              "     ambiguous_score  is_ambiguous  given_label suggested_label  \n",
              "963         1.000000         False            0            <NA>  \n",
              "31          1.000000         False            0            <NA>  \n",
              "418         1.000000         False            0            <NA>  \n",
              "163         1.000000         False            0            <NA>  \n",
              "77          0.999998         False            0            <NA>  "
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "samples_ranked_by_ambiguous_score = combined_dataset_df.sort_values(\"ambiguous_score\", ascending=False)\n",
        "\n",
        "columns_to_display = DATASET_COLUMNS + [\"ambiguous_score\", \"is_ambiguous\", \"given_label\", \"suggested_label\"]\n",
        "display(samples_ranked_by_ambiguous_score.head(5)[columns_to_display])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 608
        },
        "id": "fJ8ADqxQyKnY",
        "outputId": "8a3efe85-9243-48b7-8e5b-595f48d87f2a"
      },
      "outputs": [
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              "        document.querySelector('#df-2972d7f1-46db-4776-bd5d-dfd2ad8ede97 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
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              "        const element = document.querySelector('#df-2972d7f1-46db-4776-bd5d-dfd2ad8ede97');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
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              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-cf75cf44-4910-4a66-aa5c-1bf70fef2978 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "     Unnamed: 0  article_id                   created_date       id  \\\n",
              "266         267      316310  2017-03-02 19:12:23.522824+00  1067617   \n",
              "636         637      144327  2016-08-23 23:57:35.251063+00   444860   \n",
              "367         368      381142  2017-09-23 17:01:13.372781+00  6006998   \n",
              "106         107      322630  2017-03-28 08:11:03.591038+00  5063513   \n",
              "572         573      353118  2017-07-09 05:43:08.006149+00  5557805   \n",
              "\n",
              "     identity_attack  insult  obscene  parent_id  \\\n",
              "266              0.0     0.0      0.0    1066602   \n",
              "636              0.0     0.0      0.0     444350   \n",
              "367              0.0     0.2      0.0    6006195   \n",
              "106              0.0     0.0      0.0          0   \n",
              "572              0.0     0.3      0.0          0   \n",
              "\n",
              "                                           parent_text  publication_id  \\\n",
              "266  They are not being charged for illegal entry i...              54   \n",
              "636  What is good about this grandstand religious d...              21   \n",
              "367  The white house is a thug free zone, Trump is ...             102   \n",
              "106                                                NaN              54   \n",
              "572                                                NaN             105   \n",
              "\n",
              "     severe_toxicity  sexual_explicit  \\\n",
              "266              0.0              0.0   \n",
              "636              0.0              0.0   \n",
              "367              0.0              0.0   \n",
              "106              0.0              0.0   \n",
              "572              0.0              0.0   \n",
              "\n",
              "                                                  text  threat  outlier_score  \\\n",
              "266  They are being turned over to Border Services,...     0.0       0.719897   \n",
              "636  If you don't talk to the people the man's talk...     0.0       0.658124   \n",
              "367  How could the White House be thug free when Dr...     0.0       0.581994   \n",
              "106  One thing that I've learned over many years is...     0.0       0.508039   \n",
              "572  Or attempt to shoot one down and fail to do so...     0.4       0.502865   \n",
              "\n",
              "     is_outlier  given_label suggested_label  \n",
              "266        True            0               0  \n",
              "636        True            0               0  \n",
              "367        True            0               0  \n",
              "106        True            0               1  \n",
              "572        True            1               0  "
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "samples_ranked_by_outlier_score = combined_dataset_df.sort_values(\"outlier_score\", ascending=False)\n",
        "\n",
        "columns_to_display = DATASET_COLUMNS + [\"outlier_score\", \"is_outlier\", \"given_label\", \"suggested_label\"]\n",
        "display(samples_ranked_by_outlier_score.head(5)[columns_to_display])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6Izets7rKIeX"
      },
      "source": [
        "After analyzing the dataset consisting of **1000** samples, we discovered that there were **79** data points that were incorrectly labeled as safe, **16** data points that were marked as outliers, and **27** data points that were classified as ambiguous in nature. It's important to address these inaccuracies in order to ensure the reliability and accuracy of the data set."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I6Z-OOzOU68s"
      },
      "source": [
        "Further, we take the label correction suggestions from Cleanlab Studio and also filter out the recommended data points containing `outlier`, `ambiguous` and `near duplicates`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4I20mFGqSTDT"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "corrected_label = np.where(combined_dataset_df[\"is_label_issue\"],\n",
        "                           combined_dataset_df[\"suggested_label\"],\n",
        "                           combined_dataset_df[\"given_label\"])\n",
        "rows_to_exclude = combined_dataset_df[\"is_outlier\"] | combined_dataset_df[\"is_ambiguous\"]\n",
        "near_duplicates_to_exclude = combined_dataset_df['is_near_duplicate'] & combined_dataset_df['near_duplicate_cluster_id'].duplicated(keep='first')\n",
        "\n",
        "rows_to_exclude |= near_duplicates_to_exclude\n",
        "fixed_dataset = combined_dataset_df[[\"text\"]].copy()\n",
        "\n",
        "# Add the corrected label column\n",
        "fixed_dataset[\"label\"] = corrected_label\n",
        "fixed_dataset = fixed_dataset[~rows_to_exclude]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Wkis46KHLIXV"
      },
      "source": [
        "Now that we have cleaned the data, our next step is to operationalize it using a machine learning model.\n",
        "\n",
        "This step involves training the model on the processed data and using it to make toxicity predictions for new data."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cJvrowW2w_2f"
      },
      "source": [
        "## KerasNLP\n",
        "In this notebook, we use KerasNLP to load a pretrained neural network backbone that will be trained on our dataset via finetuning. KerasNLP is a Python library built on top of Keras, specifically designed to simplify Natural Language Processing (NLP) tasks. With its user-friendly interface and comprehensive set of functionalities, KerasNLP empowers developers and researchers to efficiently tackle a wide range of NLP challenges.\n",
        "\n",
        "### Key Features of KerasNLP:\n",
        "\n",
        "* Seamless Integration with Keras: KerasNLP seamlessly integrates with Keras, a popular deep learning library, leveraging its robust architecture and extensive ecosystem. This integration allows users to seamlessly combine NLP-specific functionalities with the flexibility and simplicity of Keras, enabling rapid prototyping and experimentation.\n",
        "\n",
        "* Modular Design: KerasNLP adopts a modular design philosophy, offering a collection of reusable building blocks for common NLP tasks such as text classification, sequence labeling, machine translation, and more. These modular components, including pre-processing utilities, model architectures, and evaluation metrics, streamline the development process and promote code reusability.\n",
        "\n",
        "* Pre-trained Models and Embeddings: One of the standout features of KerasNLP is its support for pre-trained language models and embeddings. By incorporating pre-trained models such as BERT, GPT, or ELMo, developers can leverage state-of-the-art NLP capabilities without the need for extensive training on domain-specific data. Similarly, KerasNLP provides interfaces for popular word embeddings like Word2Vec, GloVe, and FastText, enabling users to easily incorporate pre-trained embeddings into their models.\n",
        "\n",
        "* Flexibility and Customization: Despite its focus on simplicity and ease of use, KerasNLP offers a high degree of flexibility and customization. Users can customize model architectures, loss functions, and optimization strategies to suit their specific requirements. Additionally, KerasNLP provides hooks and callbacks for monitoring training progress, performing early stopping, and saving checkpoints, facilitating efficient model training and tuning.\n",
        "\n",
        "* Community Support and Documentation: KerasNLP benefits from a vibrant community of developers and researchers who contribute to its development and provide support through forums, documentation, and tutorials. The comprehensive documentation and code examples make it easy for users to get started with KerasNLP and explore its capabilities for various NLP tasks."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KUmJmBhDNEqT"
      },
      "source": [
        "## Using DeBERTaV3\n",
        "\n",
        "For our task, we use the DeBERTaV3 base model with pretrained neural network weights. DeBERTaV3 is a state-of-the-art pre-trained language model (Transformer encoder architecture) developed by Microsoft, building upon the success of previous DeBERTa and BERT models. It's quite efficient and performant, making it an attractive choice for many NLP tasks, where you don't need a many billion parameter LLM.\n",
        "\n",
        "### Key characteristics of DeBERTaV3:\n",
        "\n",
        "* Electra-style pre-training: Replaces the Mask Language Modeling (MLM) objective with Replaced Token Detection (RTD), leading to more efficient training with less data.\n",
        "* Gradient-disentangled embedding sharing: A novel method that overcomes the limitations of vanilla embedding sharing in Electra, further enhancing training efficiency and model quality.\n",
        "* State-of-the-art performance: Achieves a new state-of-the-art average score of 91.37% on the GLUE benchmark, surpassing previous models like DeBERTa and Electra.\n",
        "\n",
        "![image.png](data:image/png;base64,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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0vC8tBU9xT7H"
      },
      "source": [
        "## Training DeBERTaV3 with our cleaned dataset\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5HAmeSVCMvdz"
      },
      "source": [
        "First, we separate features (text) and labels (toxicity) corresponding to our Machine Learning task. Then, we fine-tune our neural network (DebertaV3Classifier) from the keras_nlp library to fit the data, and we demonstrate how to use the trained model to predict the toxicity of new text input."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9bK9DwkDE7or"
      },
      "outputs": [],
      "source": [
        "# Some hyperparameters to set\n",
        "BATCH_SIZE = 4\n",
        "EPOCHS = 3"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xtipE1E1YBDA",
        "outputId": "7f57fdae-8261-4bb4-b53d-b6e045f82feb"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Downloading from https://www.kaggle.com/api/v1/models/keras/deberta_v3/keras/deberta_v3_base_en/2/download/config.json...\n",
            "100%|██████████| 540/540 [00:00<00:00, 828kB/s]\n",
            "Downloading from https://www.kaggle.com/api/v1/models/keras/deberta_v3/keras/deberta_v3_base_en/2/download/model.weights.h5...\n",
            "100%|██████████| 702M/702M [00:21<00:00, 33.9MB/s]\n",
            "Downloading from https://www.kaggle.com/api/v1/models/keras/deberta_v3/keras/deberta_v3_base_en/2/download/tokenizer.json...\n",
            "100%|██████████| 424/424 [00:00<00:00, 627kB/s]\n",
            "Downloading from https://www.kaggle.com/api/v1/models/keras/deberta_v3/keras/deberta_v3_base_en/2/download/assets/tokenizer/vocabulary.spm...\n",
            "100%|██████████| 2.35M/2.35M [00:00<00:00, 4.77MB/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/3\n",
            "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m222s\u001b[0m 176ms/step - loss: 0.4291 - sparse_categorical_accuracy: 0.8591\n",
            "Epoch 2/3\n",
            "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m44s\u001b[0m 176ms/step - loss: 0.4086 - sparse_categorical_accuracy: 0.8657\n",
            "Epoch 3/3\n",
            "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m44s\u001b[0m 176ms/step - loss: 0.4037 - sparse_categorical_accuracy: 0.8657\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "<keras.src.callbacks.history.History at 0x7b8af2427910>"
            ]
          },
          "execution_count": 27,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Prepare data for ML \n",
        "features = fixed_dataset.text\n",
        "labels = fixed_dataset.label\n",
        "\n",
        "# Load pretrained neural network backbone \n",
        "classifier = keras_nlp.models.DebertaV3Classifier.from_preset(\n",
        "    \"deberta_v3_base_en\",\n",
        "    num_classes=2,\n",
        "    activation='sigmoid',\n",
        ")\n",
        "# Fine-tune the network on our data \n",
        "classifier.fit(x=features,y=labels.astype(float),batch_size=BATCH_SIZE, epochs=EPOCHS)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RHBwu72DplXZ",
        "outputId": "2581c125-5b94-4b73-bfca-de5aa875bdbc"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 175ms/step\n"
          ]
        },
        {
          "data": {
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